{"id":"W4389945137","doi":"10.1161/circ.148.suppl_1.14810","title":"Abstract 14810: ASCVD Risk Score vs Machine Learning-Based Algorithm in the Prediction of ASCVD Events in Women With Breast Cancer","year":2023,"lang":"en","type":"article","venue":"Circulation","topic":"Cardiovascular Health and Risk Factors","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Medicine; Interquartile range; Cohort; Atherosclerotic cardiovascular disease; Logistic regression; Internal medicine; Breast cancer; Receiver operating characteristic; Framingham Risk Score; Population; Cancer; Oncology; Disease","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004842842,0.0005630165,0.0006785344,0.001056332,0.0002233009,0.000878712,0.0004886865,0.0005665853,0.002563733],"category_scores_gemma":[0.01069142,0.0001466563,0.0005208252,0.0006875664,0.0002211254,0.0004422939,0.0005167956,0.0006616365,0.0004870196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005113354,"about_ca_system_score_gemma":0.001027825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001834556,"about_ca_topic_score_gemma":0.001540191,"domain_scores_codex":[0.9989354,0.0005537795,0.000103287,0.0001781234,0.0001655011,0.0000638932],"domain_scores_gemma":[0.9963604,0.002608908,0.0002866389,0.0001609491,0.0004208308,0.0001622992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005792233,0.0008094174,0.7879357,0.0001916263,0.0005910041,0.000135257,0.00009186405,0.0695425,0.001507272,0.0006188168,0.003585722,0.1291985],"study_design_scores_gemma":[0.0004271439,0.003133954,0.2010785,0.0001094861,0.0003786445,0.0004198263,0.0001216557,0.7879215,0.00299844,0.001389151,0.001986586,0.00003508592],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798266,0.0008426047,0.01599401,0.0004824185,0.0000828359,0.0002107082,0.0008475482,0.0002147217,0.001498625],"genre_scores_gemma":[0.9850299,0.0002088745,0.01255102,0.0001021854,0.00005660289,0.0001665885,0.001046722,0.00001823947,0.0008198528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004842842,"threshold_uncertainty_score":0.02561176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439643899445635,"score_gpt":0.2611541826416601,"score_spread":0.2467577436472038,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}